Figure 1.
Demonstration of strong binocular rivalry and weak pattern rivalry.
Although experiments on rivalry are typically done under controlled laboratory conditions with prisms or stereoscopes, some readers may be able to experience the effect by crossing their eyes, aligning the left and right boxes so that a total of six boxes are observed, rather than four. If done correctly, the top middle box will display dichoptic gratings, and observers experience strong rivalry, with clear alternations in dominance between leftward-oriented and rightward-oriented gratings. The bottom middle box will display binocular plaids, where each eye is shown the same two superimposed orthogonal components (leftward and rightward gratings), for which any alternations in the perceived strength of the components are very weak.
Figure 2.
(A) Schematic. Monocular neurons drive iso-oriented binocular summation neurons with excitatory feedforward connections (green). Mutual inhibition within each layer is implemented by a normalization pool (gray shadows). (B–D) Model simulations. Top row: dichoptic gratings. Middle Row: monocular plaid. Bottom row: binocular plaids. (B) Stimulus conditions. (C) Example response time-courses of the two binocular summation neurons. (D) Winner-take-all (WTA) index. The conventional model shows dichoptic grating rivalry that is only slightly stronger than plaid component rivalry.
Figure 3.
Reliability of simulated binocular layer responses to monocular gratings.
(A) Example responses to a monocular grating for one of the 6 parameterizations of the conventional model that passed the initial two criteria (see Methods). Magenta, simulated response time-course for a neuron tuned to the orientation of the monocular grating. Cyan, simulated response time-course for a neuron with orthogonal orientation preference. While this parameterization of the model produced stronger rivalry for dichoptic gratings compared to plaids, the model behaved implausibly when presented with a monocular grating. Specifically, the model occasionally showed stronger responses for the non-presented orthogonal grating. (B) Simulated responses of the opponency model were stable, and did not any show any switches in dominance.
Figure 4.
Demonstration that a previously published model (Wilson, 2003) does not show weaker rivalry for binocular plaids and monocular plaids.
(A) Standard rivalry under dichoptic conditions. Our simulation results are identical to those of Figure 2A in Wilson (2003). (B) Binocular plaid conditions result in full rivalry. (C) Monocular plaid conditions results in sustained dominance.
Figure 5.
(A) Schematic. An opponency neuron computes a response difference between the two eyes for a particular orientation preference and retinotopic location. The opponency neuron inhibits activity in the opposite eye (curved red line), thus amplifying the winner-take-all behavior of normalization (gray shadow). Not shown are three other opponency neurons (a R-L neuron selective for the orthogonal orientation, and two L-R neurons). Also not shown are the two normalization pools for the opponency neurons (one for R-L opponency neurons, and another for L-R opponency neurons). (B–D) Model simulations (same format as Figure 2). The opponency model shows dichoptic grating rivalry that is much stronger than plaid component rivalry.
Table 1.
Ocular opponency model parameters.
Table 2.
Parameter values used in the conventional model grid search.
Figure 6.
Schematic of a block from the psychophysics experiment.
In the binocular adaptation condition (left sequence), the gratings were shown to both eyes. In the monocular adaptation condition (right sequence), the gratings were shown to only one eye at a time, alternating at 0.94 Hz. After the adaptation period, observers viewed orthogonal (rival) stimuli in each eye for 80 sec and reported their percepts with button presses.
Figure 7.
Model predictions and psychophysical results from the adaptation experiment.
(A) Conventional model predictions. (B) Opponency model predictions. (C) Psychophysical results. Error bars are the standard error for repeated measures. (Standard error after each observer's mixed percept fractions were recentered to the mean across observers and conditions.)